Blind iterative maximum likelihood‐based frequency and transition time estimation for frequency hopping systems

Fu, Kuo Ching; Chen, Yung‐Fang

doi:10.1049/iet-com.2012.0557

Cited by 3 publications

(1 citation statement)

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“…Among various methods of parameter estimation, estimation methods based on maximum likelihood (ML) are practically intractable with few or no prior information, and thus several ML based methods have been proposed [6,7]. Recently, another two techniques have attracted a lot of attentions, that is, time-frequency analysis and sparse reconstruction, since they can estimate the parameters by recovering clear time-frequency spectrum.…”

Section: Introductionmentioning

confidence: 99%

A Parameter Estimation Algorithm for Multiple Frequency‐Hopping Signals Based on Sparse Bayesian Method

Zhang¹,

Guo²,

Qi³

2017

Mathematical Problems in Engineering

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Parameter estimation and network sorting for noncooperative wideband frequency-hopping (FH) signals have been essential and challenging tasks, especially in the case with little or even no prior information at all. In this paper, we propose a nearly blind estimation approach to estimate signal parameters based on sparse Bayesian reconstruction. Taking the sparsity in the spatial frequency domain of multiple FH signals into account, we propose a sparse Bayesian algorithm to estimate the spatial frequency parameters. As a result, the frequency and direction of arrival (DOA) parameters can be obtained. In order to improve the accuracy of the estimation parameters, we employ morphological filter methods to further clean the data poisoned by the noise. Moreover, our method is applicable to the wideband signal models with little prior information. We also conduct extensive numerical simulations to verify the performance of our method. Notably, the proposed method works well even in low signal-to-noise ratio (SNR) environment.

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Section: Introductionmentioning

confidence: 99%